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Transcribe (whisper) and translate (gpt) voice into LRC file.

Project description

Open-Lyrics

Open-Lyrics is a Python library that transcribes voice files using faster-whisper, and translates/polishes the resulting text into .lrc files in the desired language using OpenAI-GPT.

Installation

  1. Please install CUDA and cuDNN first according to https://opennmt.net/CTranslate2/installation.html to enable faster-whisper.

  2. Add your OpenAI API key to environment variable OPENAI_API_KEY.

  3. Install whisperx

    pip install git+https://github.com/m-bain/whisperx.git
    
  4. This project can be installed from PyPI:

    pip install openlrc
    

    or install directly from GitHub:

    pip install git+https://github.com/zh-plus/Open-Lyrics
    

Usage

from openlrc import LRCer

lrcer = LRCer()

# Single file
lrcer.run('./data/test.mp3', target_lang='zh-cn')  # Generate translated ./data/test.lrc with default translate prompt.

# Multiple files
lrcer.run(['./data/test1.mp3', './data/test2.mp3'], target_lang='zh-cn')
# Note we run the transcription sequentially, but run the translation concurrently for each file.

Todo

  • [Efficiency] Batched translate/polish for GPT request (enable contextual ability).
  • [Efficiency] Concurrent support for GPT request.
  • [Efficiency & Transcription Quality] Use whisperx for transcription.
  • [Translation Quality] Make translate prompt more robust according to https://github.com/openai/openai-cookbook.
  • [Usability] Automatically fix json encoder error using GPT.
  • [Efficiency] Asynchronously perform transcription and translation for multiple audio inputs.
  • [Quality] Improve batched translation/polish prompt according to gpt-subtrans.
  • [Usability] Multiple output format support.
  • [Efficiency] Add Azure OpenAI Service support.
  • [Usability] Add local LLM support.
  • [Others] Add transcribed examples.
    • Song
    • Podcast
    • Audiobook

Credits

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